{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "卫星图像识别.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "xdYAsbYMvOVI",
        "WXJeCQPZvbZ-",
        "_e2ADhm0Gf80",
        "qkf5JAPtKu0A"
      ],
      "authorship_tag": "ABX9TyPSZZFLdmoFNrq+9PvYOoQ7",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sunyingjian/study-tensorflow/blob/master/%E5%8D%AB%E6%98%9F%E5%9B%BE%E5%83%8F%E8%AF%86%E5%88%AB.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h4qWNsAX3nfg",
        "colab_type": "code",
        "outputId": "e9c10189-17cf-4add-c7f2-b90a5bc20d89",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "import os\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "path = \"/content/drive/My Drive/dataset\"\n",
        "os.chdir(path)\n",
        "os.listdir(path)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['2_class']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lT1IAsLn41u-",
        "colab_type": "code",
        "outputId": "8f934c0c-2355-4fa8-9dc6-7a5379a32f53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        }
      },
      "source": [
        "%tensorflow_version 2.x\n",
        "import tensorflow as tf\n",
        "print('tensorflow:',format(tf.__version__))\n",
        "tf.config.list_physical_devices('GPU')\n",
        "tf.test.is_gpu_available()\n",
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "from keras import layers\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "import pathlib"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n",
            "tensorflow: 2.1.0\n",
            "WARNING:tensorflow:From <ipython-input-3-4af11a6597de>:5: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use `tf.config.list_physical_devices('GPU')` instead.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VNDxqHEa5afE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_dir='/content/drive/My Drive/dataset/2_class'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9R19Tmoj72H_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_root=pathlib.Path(data_dir)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-TDdxSPJ9ujv",
        "colab_type": "code",
        "outputId": "f902eb30-54d1-4332-9b9e-6ad0752d8e41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "for item in data_root.iterdir():\n",
        "  print(item)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/dataset/2_class/airplane\n",
            "/content/drive/My Drive/dataset/2_class/lake\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7wH3-FTg-l5O",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_image_path=list(data_root.glob('*/*'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4A8StwguX68J",
        "colab_type": "code",
        "outputId": "c71e2124-9869-4b73-8d89-fd8be097efa0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "len(all_image_path)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1400"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yrv1wjMAYs0c",
        "colab_type": "code",
        "outputId": "220d6dd8-3f16-47db-9685-c4ffb22eb7db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "all_image_path=[str(path) for path in all_image_path]\n",
        "all_image_path[10:12]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/My Drive/dataset/2_class/airplane/airplane_012.jpg',\n",
              " '/content/drive/My Drive/dataset/2_class/airplane/airplane_013.jpg']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mnz8ik1J218l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import random\n",
        "random.shuffle(all_image_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gha8G8MO3ie5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "image_count=len(all_image_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VOWgai8f9Et6",
        "colab_type": "code",
        "outputId": "4d61b72d-ae13-4377-bb80-a8fab5873bdf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "label_names=sorted(item.name for item in data_root.glob('*/'))\n",
        "label_names"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['airplane', 'lake']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ph9UuLhl-Zqt",
        "colab_type": "code",
        "outputId": "4e9b43ae-71d6-4fb2-a4f5-9bcb97613aef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "label_to_index=dict((name,index) for index,name in enumerate(label_names))\n",
        "label_to_index"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'airplane': 0, 'lake': 1}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OESFOKgk7AKb",
        "colab_type": "code",
        "outputId": "73c0735e-8bdf-4864-f380-71b2b3e6d7d4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#获取路径来实现对图片做标签,通过下面的代码生成一个pathlib对象,那么他的上一层目录就剩他的label\n",
        "pathlib.Path('/content/drive/My Drive/dataset/2_class/airplane/airplane_012.jpg').parent.name"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'airplane'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1w5fz3FW-GBP",
        "colab": {}
      },
      "source": [
        "#通过推导式 labe_to_index得到分组的编号\n",
        "all_image_labe=[label_to_index[pathlib.Path(p).parent.name]for p in all_image_path]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3MEGI7hgHz7a",
        "colab_type": "code",
        "outputId": "be89a850-72be-4bb3-819e-6dd7f0434ea6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "all_image_labe[:5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 1, 1, 1, 0]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ujJ0N5ZqH4zu",
        "colab_type": "code",
        "outputId": "a884f284-4e24-4212-8d36-2a7448d5da1c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "all_image_path[:5]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['/content/drive/My Drive/dataset/2_class/lake/lake_682.jpg',\n",
              " '/content/drive/My Drive/dataset/2_class/lake/lake_017.jpg',\n",
              " '/content/drive/My Drive/dataset/2_class/lake/lake_224.jpg',\n",
              " '/content/drive/My Drive/dataset/2_class/lake/lake_003.jpg',\n",
              " '/content/drive/My Drive/dataset/2_class/airplane/airplane_040.jpg']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "whLsm65JIR1E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import IPython.display as display\n",
        "index_to_label=dict((v,k) for k,v in label_to_index.items())\n",
        "#这样我们就可以通过这个字典获取到 "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "arMNvCgnxQB8",
        "colab_type": "text"
      },
      "source": [
        "#真正的代码从下面开始"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sdyM80fAQlF0",
        "colab_type": "code",
        "outputId": "e4758fc6-e4a3-47ec-f486-412bd0db89e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 888
        }
      },
      "source": [
        "for n in range(3):\n",
        "  image_index=random.choice(range(len(all_image_path)))\n",
        "  display.display(display.Image(all_image_path[image_index]))\n",
        "  print(index_to_label[all_image_labe[image_index]])\n",
        "  print()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "lake\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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4OQQOlRzSpbxPKSflGalOBwelQzQLPA8TOUVwRuABI9xSKOIup7rxJqqWyKXZ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\n+4lQd25f4AR09h60MD//2Q==\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "airplane\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/jpeg": 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gf1qqdUms7wLHKkqDG5A3BFbKOhg56nqEl4LdJDHKXjjHzN5gAX8K4ee+m1fU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\neKrQnV6FGzd7JnZo4p45MIo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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "lake\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RI8WcVKRu2eS",
        "colab_type": "text"
      },
      "source": [
        "#单张图片的处理过程\n",
        "##读取图片"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y3vqjrexTvT0",
        "colab_type": "code",
        "outputId": "96bb0f85-3a5c-4354-adb9-01ebde3e2a1f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "#根据路径，读取图片\n",
        "img_path=all_image_path[0]\n",
        "img_path\n",
        "img_raw=tf.io.read_file(img_path)#读取图片\n",
        "img_raw"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xdYAsbYMvOVI",
        "colab_type": "text"
      },
      "source": [
        "##解码图片"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5Hd96jiuWE4f",
        "colab_type": "code",
        "outputId": "69e372b2-d55f-44d0-e77e-9af037f9fd63",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        }
      },
      "source": [
        "#解码图片\n",
        "image_tensor=tf.image.decode_jpeg(img_raw,channels=3)\n",
        "image_tensor=tf.image.resize(image_tensor,[256,256])\n",
        "image_tensor"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(256, 256, 3), dtype=float32, numpy=\n",
              "array([[[ 50.,  80.,  56.],\n",
              "        [ 45.,  75.,  51.],\n",
              "        [ 50.,  80.,  56.],\n",
              "        ...,\n",
              "        [ 51.,  76., 116.],\n",
              "        [ 47.,  75., 115.],\n",
              "        [ 46.,  73., 116.]],\n",
              "\n",
              "       [[ 50.,  80.,  56.],\n",
              "        [ 45.,  75.,  51.],\n",
              "        [ 49.,  79.,  55.],\n",
              "        ...,\n",
              "        [ 71.,  96., 137.],\n",
              "        [ 69.,  96., 141.],\n",
              "        [ 69.,  96., 141.]],\n",
              "\n",
              "       [[ 49.,  79.,  55.],\n",
              "        [ 44.,  74.,  50.],\n",
              "        [ 46.,  76.,  52.],\n",
              "        ...,\n",
              "        [ 75.,  98., 148.],\n",
              "        [ 71.,  97., 148.],\n",
              "        [ 70.,  96., 147.]],\n",
              "\n",
              "       ...,\n",
              "\n",
              "       [[ 69.,  88.,  66.],\n",
              "        [ 70.,  89.,  67.],\n",
              "        [ 72.,  91.,  69.],\n",
              "        ...,\n",
              "        [ 69.,  89.,  64.],\n",
              "        [ 69.,  89.,  64.],\n",
              "        [ 69.,  89.,  64.]],\n",
              "\n",
              "       [[ 71.,  90.,  68.],\n",
              "        [ 70.,  89.,  67.],\n",
              "        [ 69.,  88.,  66.],\n",
              "        ...,\n",
              "        [ 73.,  93.,  68.],\n",
              "        [ 72.,  92.,  67.],\n",
              "        [ 70.,  90.,  65.]],\n",
              "\n",
              "       [[ 68.,  87.,  65.],\n",
              "        [ 65.,  84.,  62.],\n",
              "        [ 61.,  80.,  58.],\n",
              "        ...,\n",
              "        [ 77.,  97.,  72.],\n",
              "        [ 76.,  96.,  71.],\n",
              "        [ 73.,  93.,  68.]]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WXJeCQPZvbZ-",
        "colab_type": "text"
      },
      "source": [
        "##转换数据类型"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3_FWmCYMIeJ",
        "colab_type": "code",
        "outputId": "05351dd9-4723-4c1e-efc9-9e70c0ac4db3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "image_tensor=tf.cast(image_tensor,tf.float32)#转换数据类型\n",
        "#图片标准化处理\n",
        "image_tensor=image_tensor/255\n",
        "#我们查看下这个图片,看到底是不是范围是0-1\n",
        "image_tensor.numpy().max()\n",
        "image_tensor.numpy().min()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dnqJhV0FqNFI",
        "colab_type": "text"
      },
      "source": [
        "以上呢，是对单一图片的整个处理方式：\n",
        "\n",
        "1. 读取这张图片\n",
        "\n",
        "2. 对图片进行解码\n",
        "\n",
        "3. 转换图片数据类型与标准化\n",
        "\n",
        "\n",
        "接下来，我们写一个函数来对整个图片数据集进行处理："
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LIkc5HEFsLK1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_preprosess_image(image_path):\n",
        "  img_raw=tf.io.read_file(img_path)#读取路径\n",
        "  image_tensor=tf.image.decode_jpeg(img_raw,channels=3)#解码图片,3代表解析的是彩色图片\n",
        "  image_tensor=tf.image.resize(image_tensor,size=[256,256])#图片可能会变形,告诉tensorflow大小是256×256\n",
        "  image_tensor=tf.cast(image_tensor,tf.float32)#转换数据类型\n",
        "  image_tensor=image_tensor/255#标准化操作\n",
        "  return image_tensor #返回处理结果"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vYf9NXnQwWkS",
        "colab_type": "code",
        "outputId": "53288676-bf1e-4517-d988-79dd8fa9e287",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "image_path_test=all_image_path[100]\n",
        "plt.imshow(load_preprosess_image(image_path_test))#调用并画出来"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f5e803eec88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YcpGTOfI+97nz33ONNyfsnIGMcKolEN9lGs7JUHMtDUz3atskxXPKti6LEfU/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moAaKj4+vFET0DQ9LOlYQWA6J2k1GJrxQkwdY7sW2uxXC4xjJMh9IauQF3XPg0nvcz59Lp+0Q5j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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_e2ADhm0Gf80",
        "colab_type": "text"
      },
      "source": [
        "##构造tf.data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rsrExK5eGezs",
        "colab_type": "code",
        "outputId": "1797b55f-faf0-4516-c46b-4f20f37c79ee",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "path_ds=tf.data.Dataset.from_tensor_slices(all_image_path)\n",
        "image_dataset=path_ds.map(load_preprosess_image)#建造数据集\n",
        "image_dataset"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<MapDataset shapes: (256, 256, 3), types: tf.float32>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VVUV_8hxrUE6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#对image_dataset的类型进行转换，我们知道他的图片格式是256×256×3\n",
        "#为什么他没有显示出来呢"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qkf5JAPtKu0A",
        "colab_type": "text"
      },
      "source": [
        "##构造label"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfSzCt9uKtqV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "label_dataset=tf.data.Dataset.from_tensor_slices(all_image_labe)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "om-Dcsp-MUdM",
        "colab_type": "text"
      },
      "source": [
        "##测试下标签"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "agAgBJ5kMY-b",
        "colab_type": "code",
        "outputId": "e9fcaa74-ffea-4c9b-dbd6-b12cbcedaa09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for label in label_dataset:\n",
        "  print(label)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
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            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
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            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
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            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(1, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n",
            "tf.Tensor(0, shape=(), dtype=int32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HqmuDjIyTcMD",
        "colab_type": "code",
        "outputId": "493036a8-4f3d-4097-d72e-f14a642ebaa6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "#合并数据\n",
        "dataset=tf.data.Dataset.zip((image_dataset,label_dataset))\n",
        "#我们是以元组的形式进行zip的\n",
        "dataset"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<ZipDataset shapes: ((256, 256, 3), ()), types: (tf.float32, tf.int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BwszhCt0zcYT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#划分train与test数据\n",
        "test_count=int(image_count/5)\n",
        "#必须是个整数所以我们加int\n",
        "train_count=image_count-test_count"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sAzEf9v2gJVi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_dataset=dataset.skip(test_count)\n",
        "#skip就是掠过的意思\n",
        "#得到train_dataset的个数\n",
        "test_dataset=dataset.take(test_count)\n",
        "#take就是取的意思"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C4k1Oxrplqov",
        "colab_type": "code",
        "outputId": "320745ca-d385-479d-8f20-6aa292e396df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "BATCH_SIZE=32\n",
        "train_dataset=train_dataset.shuffle(buffer_size=train_count)\n",
        "train_dataset=train_dataset.batch(BATCH_SIZE)\n",
        "train_dataset"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BatchDataset shapes: ((None, 256, 256, 3), (None,)), types: (tf.float32, tf.int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sz4fflzkoewM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model=tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.Conv2D(64,(3,3),\n",
        "                 input_shape=(256,256,3),\n",
        "                 activation='relu'))\n",
        "model.add(tf.keras.layers.Conv2D(64,(3,3),activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D())\n",
        "model.add(tf.keras.layers.Dropout(0.3))\n",
        "model.add(tf.keras.layers.Conv2D(128,(3,3),activation='relu'))\n",
        "model.add(tf.keras.layers.Conv2D(128,(3,3),activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D())\n",
        "model.add(tf.keras.layers.Dropout(0.3))\n",
        "model.add(tf.keras.layers.Conv2D(256,(3,3),activation='relu'))\n",
        "model.add(tf.keras.layers.Conv2D(256,(3,3),activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D())\n",
        "model.add(tf.keras.layers.Dropout(0.3))\n",
        "model.add(tf.keras.layers.Conv2D(512,(3,3),activation='relu'))\n",
        "model.add(tf.keras.layers.Conv2D(512,(3,3),activation='relu'))\n",
        "model.add(tf.keras.layers.MaxPooling2D())\n",
        "model.add(tf.keras.layers.Dropout(0.3))\n",
        "model.add(tf.keras.layers.Conv2D(1024,(3,3),activation='relu'))\n",
        "model.add(tf.keras.layers.GlobalAveragePooling2D())\n",
        "model.add(tf.keras.layers.Dense(1024,activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(256,activation='relu'))\n",
        "model.add(tf.keras.layers.Dense(1,activation='sigmoid'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mmgx92t7wtWR",
        "colab_type": "code",
        "outputId": "575b941b-a551-42cc-9898-50cfe088cc5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        }
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d_9 (Conv2D)            (None, 254, 254, 64)      1792      \n",
            "_________________________________________________________________\n",
            "conv2d_10 (Conv2D)           (None, 252, 252, 64)      36928     \n",
            "_________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2 (None, 126, 126, 64)      0         \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 126, 126, 64)      0         \n",
            "_________________________________________________________________\n",
            "conv2d_11 (Conv2D)           (None, 124, 124, 128)     73856     \n",
            "_________________________________________________________________\n",
            "conv2d_12 (Conv2D)           (None, 122, 122, 128)     147584    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2 (None, 61, 61, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 61, 61, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_13 (Conv2D)           (None, 59, 59, 256)       295168    \n",
            "_________________________________________________________________\n",
            "conv2d_14 (Conv2D)           (None, 57, 57, 256)       590080    \n",
            "_________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2 (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 28, 28, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_15 (Conv2D)           (None, 26, 26, 512)       1180160   \n",
            "_________________________________________________________________\n",
            "conv2d_16 (Conv2D)           (None, 24, 24, 512)       2359808   \n",
            "_________________________________________________________________\n",
            "max_pooling2d_7 (MaxPooling2 (None, 12, 12, 512)       0         \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 12, 12, 512)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_17 (Conv2D)           (None, 10, 10, 1024)      4719616   \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d_1 ( (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1024)              1049600   \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 256)               262400    \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 257       \n",
            "=================================================================\n",
            "Total params: 10,717,249\n",
            "Trainable params: 10,717,249\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_VhogvaUycnl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#编译模型\n",
        "model.compile(optimizer='adam',\n",
        "                          loss='binary_crossentropy',\n",
        "                          metrics=['acc'] )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DdMyh0DezW8U",
        "colab_type": "text"
      },
      "source": [
        "##确定epoch的数量\n",
        "\n",
        "所以我们要告诉他一个batch是多少步所以要这么算，\n",
        "这样告诉模型我们训练要多少个epoch"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJhTVtGvzbfW",
        "colab_type": "code",
        "outputId": "79556456-66d5-4b59-dab3-4e934b8de99c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "steps_per_epoch=train_count//BATCH_SIZE\n",
        "validation_steps=test_count//BATCH_SIZE\n",
        "validation_steps"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uC4YMg7czzBX",
        "colab_type": "text"
      },
      "source": [
        "##运行模型"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a5XU9ayW5iFo",
        "colab_type": "text"
      },
      "source": [
        "这个地方我使用视频中的代码会报错，所以我没有使用数据集，也没有使用validation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oJ7sBdP_zyMy",
        "colab_type": "code",
        "outputId": "2a5bce5e-9bcd-4182-d511-5fbaa9b4d9a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "history=model.fit(train_dataset,epochs=30\n",
        "                )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train for 35 steps\n",
            "Epoch 1/30\n",
            "35/35 [==============================] - 14s 402ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 2/30\n",
            "35/35 [==============================] - 14s 401ms/step - loss: 0.6932 - acc: 0.5071\n",
            "Epoch 3/30\n",
            "35/35 [==============================] - 14s 402ms/step - loss: 0.6936 - acc: 0.5071\n",
            "Epoch 4/30\n",
            "35/35 [==============================] - 14s 404ms/step - loss: 0.6933 - acc: 0.4911\n",
            "Epoch 5/30\n",
            "35/35 [==============================] - 14s 406ms/step - loss: 0.6933 - acc: 0.5071\n",
            "Epoch 6/30\n",
            "35/35 [==============================] - 14s 412ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 7/30\n",
            "35/35 [==============================] - 14s 412ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 8/30\n",
            "35/35 [==============================] - 14s 412ms/step - loss: 0.6933 - acc: 0.5071\n",
            "Epoch 9/30\n",
            "35/35 [==============================] - 14s 413ms/step - loss: 0.6932 - acc: 0.5071\n",
            "Epoch 10/30\n",
            "35/35 [==============================] - 15s 415ms/step - loss: 0.6933 - acc: 0.5071\n",
            "Epoch 11/30\n",
            "35/35 [==============================] - 15s 418ms/step - loss: 0.6932 - acc: 0.5071\n",
            "Epoch 12/30\n",
            "35/35 [==============================] - 15s 419ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 13/30\n",
            "35/35 [==============================] - 15s 418ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 14/30\n",
            "35/35 [==============================] - 15s 418ms/step - loss: 0.6932 - acc: 0.5071\n",
            "Epoch 15/30\n",
            "35/35 [==============================] - 15s 418ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 16/30\n",
            "35/35 [==============================] - 15s 419ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 17/30\n",
            "35/35 [==============================] - 15s 420ms/step - loss: 0.6932 - acc: 0.5071\n",
            "Epoch 18/30\n",
            "35/35 [==============================] - 15s 420ms/step - loss: 0.6930 - acc: 0.5071\n",
            "Epoch 19/30\n",
            "35/35 [==============================] - 15s 420ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 20/30\n",
            "35/35 [==============================] - 15s 421ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 21/30\n",
            "35/35 [==============================] - 15s 421ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 22/30\n",
            "35/35 [==============================] - 15s 422ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 23/30\n",
            "35/35 [==============================] - 15s 421ms/step - loss: 0.6932 - acc: 0.5071\n",
            "Epoch 24/30\n",
            "35/35 [==============================] - 15s 422ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 25/30\n",
            "35/35 [==============================] - 15s 422ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 26/30\n",
            "35/35 [==============================] - 15s 421ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 27/30\n",
            "35/35 [==============================] - 15s 421ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 28/30\n",
            "35/35 [==============================] - 15s 420ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 29/30\n",
            "35/35 [==============================] - 15s 421ms/step - loss: 0.6931 - acc: 0.5071\n",
            "Epoch 30/30\n",
            "35/35 [==============================] - 15s 420ms/step - loss: 0.6931 - acc: 0.5071\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}